made some functions private and added author

Author: TRY-ER

package/AUTHORS

@@ -246,11 +246,12 @@ Chronological list of authors
   - Matthew Davies
   - Jia-Xin Zhu
   - Tanish Yelgoe
- 2025
+2025
   - Joshua Raphael Uy 
   - Namir Oues
   - Lexi Xu
   - BHM-Bob G    
+  - Debasish Mohanty
 
 External code
 -------------

package/MDAnalysis/visualization/streamlines.py

@@ -177,8 +177,8 @@ def split_grid(grid, num_cores):
     ]
 
 
-# some utility functions for trajectory iteration
-def produce_list_indices_point_in_polygon_this_frame(
+# some private utility functions for trajectory iteration
+def _produce_list_indices_point_in_polygon_this_frame(
     vertex_coord_list, relevant_particle_coordinate_array_xy
 ):
     """
@@ -197,7 +197,7 @@ def produce_list_indices_point_in_polygon_this_frame(
     return list_indices_point_in_polygon
 
 
-def produce_list_centroids_this_frame(
+def _produce_list_centroids_this_frame(
     list_indices_in_polygon, relevant_particle_coordinate_array_xy
 ):
     """
@@ -253,14 +253,14 @@ def per_core_work(
         # only 2D / xy coords for now
         # I will need a list of indices for relevant particles falling within each square in THIS frame:
         list_indices_in_squares_this_frame = (
-            produce_list_indices_point_in_polygon_this_frame(
+            _produce_list_indices_point_in_polygon_this_frame(
                 list_square_vertex_arrays_this_core,
                 relevant_particle_coordinate_array_xy,
             )
         )
         # likewise, I will need a list of centroids of particles in each square (same order as above list):
         list_centroids_in_squares_this_frame = (
-            produce_list_centroids_this_frame(
+            _produce_list_centroids_this_frame(
                 list_indices_in_squares_this_frame,
                 relevant_particle_coordinate_array_xy,
             )
@@ -270,7 +270,7 @@ def per_core_work(
         ):  # if the previous frame had indices in at least one square I will need to use
             #  those indices to generate the updates to the corresponding centroids in this frame:
             list_centroids_this_frame_using_indices_from_last_frame = (
-                produce_list_centroids_this_frame(
+                _produce_list_centroids_this_frame(
                     list_previous_frame_indices,
                     relevant_particle_coordinate_array_xy,
                 )

testsuite/MDAnalysisTests/visualization/test_streamlines.py

@@ -71,7 +71,7 @@ def test_produce_list_indices_point_in_polygon_this_frame():
     points = np.array([[0.5, 0.5], [1.5, 1.5], [2.5, 2.5], [3.5, 3.5]])
 
     # Call the function under test.
-    result = streamlines.produce_list_indices_point_in_polygon_this_frame(
+    result = streamlines._produce_list_indices_point_in_polygon_this_frame(
         vertex_list, points
     )
 
@@ -90,7 +90,7 @@ def test_produce_list_centroids_empty():
     list_indices = [(np.array([]),)]
     # Dummy particle coordinate array (won't be used since indices is empty)
     pts = np.array([[0, 0], [1, 1]])
-    result = streamlines.produce_list_centroids_this_frame(list_indices, pts)
+    result = streamlines._produce_list_centroids_this_frame(list_indices, pts)
     assert result == [None]
 
 
@@ -100,7 +100,7 @@ def test_produce_list_centroids_single_square():
     # Choose indices that pick points [1] and [3]
     indices_tuple = (np.array([1, 3]),)
     list_indices = [indices_tuple]
-    result = streamlines.produce_list_centroids_this_frame(list_indices, pts)
+    result = streamlines._produce_list_centroids_this_frame(list_indices, pts)
     expected = np.average(pts[[1, 3]], axis=0)
     np.testing.assert_array_almost_equal(result[0], expected)
 
@@ -112,7 +112,7 @@ def test_produce_list_centroids_multiple_squares():
     # Second square will use pts[1], pts[3] and pts[4]
     indices2 = (np.array([1, 3, 4]),)
     list_indices = [indices1, indices2]
-    result = streamlines.produce_list_centroids_this_frame(list_indices, pts)
+    result = streamlines._produce_list_centroids_this_frame(list_indices, pts)
     expected1 = np.average(pts[[0, 2]], axis=0)
     expected2 = np.average(pts[[1, 3, 4]], axis=0)
     np.testing.assert_array_almost_equal(result[0], expected1)